Stationary wireless sensor networks (WSNs) fail to scale when the area to be monitored is unbounded and the physical phenomenon to be monitored may migrate through a large region. Deploying mobile sensor networks (MSNs) alleviates this problem, as the self-configuring MSN can relocate to follow the phenomenon of interest. However, a major challenge here is to maximize the sensing coverage in an unknown, noisy, and dynamically changing environment with nodes having limited sensing range and energy, and moving under distributed control. To address these challenges, we propose a new distributed algorithm, Causataxis, which enables the MSN to relocate toward the interesting regions and adjust its shape and position as the sensing environment changes. (In Latin, causa means motive/interest. A taxis (plural taxes) is an innate behavioral response by an organism to a directional stimulus. We use Causataxis to refer to an interest driven relocation behavior.) Unlike conventional cluster-based systems with backbone networks, a unique feature of our proposed approach is its biosystem inspired growing and rotting behaviors with coordinated locomotion. We compare Causataxis with a swarm-based algorithm, which uses the concept of virtual spring forces to relocate mobile nodes based on local neighborhood information. Our simulation results show that Causataxis outperforms the swarm-based algorithm in terms of the sensing coverage, the energy consumption, and the noise tolerance with a slightly high communication overhead.